Explainable Machine Learning Model to Prediction EGFR Mutation in Lung Cancer.

ABSTRACT:

Objectives

The aim of this study is to determine whether the clinical features including blood markers can establish an explainable machine learning model to predict epidermal growth factor receptor (EGFR) mutation in lung cancer.

Methods

We retrospectively analyzed 7,413 patients with lung adenocarcinoma (LA) diagnosed by gene sequencing in West China Hospital of the Sichuan University from April 2015 to June 2019. The machine learning algorithms (MLAs) included logistic regression (LR), random forest (RF), LightGBM, support vector machine (SVM), multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and decision tree (DT). Demographic characteristics, personal history, and blood markers were taken into. The area under the receiver operating characteristic curve

SUBMITTER: Yang R 

PROVIDER: S-EPMC9259982 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature